{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x400 with 0 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b468036d8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b468a52b0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b3c8a6908>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-3.455751918948773, 3.455751918948773)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x23b4688cdd8>,\n  <matplotlib.axis.XTick at 0x23b467b6c18>,\n  <matplotlib.axis.XTick at 0x23b468a5f28>,\n  <matplotlib.axis.XTick at 0x23b468a6438>,\n  <matplotlib.axis.XTick at 0x23b468a6908>],\n <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-1.1, 1.0999165211263138)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x23b4688c7b8>,\n  <matplotlib.axis.YTick at 0x23b467b6c50>,\n  <matplotlib.axis.YTick at 0x23b468a6b38>],\n <a list of 3 Text yticklabel objects>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n8wPga9asWaqq66+yZ2cDRasdwrRpwKSPXJwx2LmTD9aoYS8tiP+pWpWzOpb27VVB0YeUHIikwJo1bD9v+egjvnaJ/6pQAZg0yb5e02mSffeZOzfH2oDo39q04cYRgMUrks76iFcpORC5gbNngaeesou2deqk0shO8fTTXAqqgvUYc7GL/RuTJgElS5oLTFLGqnJVoQKv//4beO01szEFiRBX6qtQqWyVBJW2be3j17ffzvr9gXrDWbRoUezfv990GB4Vd+IcDtxSA6UubAYALKrwEhpsVoEdR9mwge04rWJVM2bYu04lNVK8Q0ozByLX8dVXdmKQPTs3UQdqYhCoMr/e+XJi8DeqocmWsZg503BQkjpVqrjvP+jQAdi2zVw8QUDJgcg1/POP+4m3d98FypQxF4+kwfTpLMULICFzdjyJrxCHLOjQAdi713BskjpJ9x+cPctiI1a3M/E4JQciybh0ia89p07x+umnWa9fHGT3bp5h/E/YR+/j1qfYgvnkSX5/L10yFZykmrX/oFw5Xq9aBQwebDamAKbkQCQZQ4ZwbwEAhIcD77+vegaOcukS0Lo17zABoHVrhDzXEu+/zx4/ACsnDx1qLkRJgxw5WN0yNJTXgwcDy5ebjSlAKTkQucKyZcCAARxnyAB89pnqGTjOmDF234TwcGD8eABszPTZZ3b/hQEDgD/+MBOipFHNmkDfvhwnJnKpwUoCxWOUHIgkcfYsX2usdr/9+rFZnzjIunVAVBTHISHAJ58AuXJd/u0777TfW6zlI723OEyfPkDt2hzv3KnjjV6g5EAkiR49uFQN8E2kTx+z8UgqxcXx3T4+ntevvw7Uq3fVH3vzTX5/AX6/e/b0YYySfmFhXF6wSmBOmgTMnWs2pgCjOgci//n5Z7sZXLZsvAEtXdpsTL7m+DoHPXsCo0ZxXLUqN61d4+zpzp1AtWp2U6affwbuu89HcYpnTJpk98a46SZg/XqgUCGzMfk31TkQSY1Tp1jsyDJqVPAlBo63ZAkwejTHGTPyzvI6RSlKlwZGjrSv27ZlXyZxkLZtgYcf5vjYMSYKqb/hlWQoORABO8Lu28fxPfcAL75oNh5JpTNneDrBemMYPBj43/9u+Ndeeglo0IDjvXvVGdhxrBacN9/M6/nzWT1R0k3LChL0FiwAHniA45w5OTNpHXcLNo5dVujUiWfgAaBuXWDRIvu42w3s2cMVCGtT4oIFQJMm3glTvGTOHODRRznOlw/YuFHLC8nTsoJISsTE2EuWADB2bPAmBo61ZImdGGTLBkydmuLEAOBJx7Fj7et27fi8EAdp3pzd0QDgxAl225J0UXIgQa1LF+DgQY4bN3bfdyAOEBvLd3PLkCFAqVKp/jTt2gGNGnF88CDw6qseik98Z8IEbkoEgFmzoAYa6aPkQILWd9+x9D7AIkcffaQqiI7Tvz+wfTvHd9wBdO6cpk8TEsKN71axq08/5fKCOEiBAu7NmTp14iZFSRMlBxKUzpxxK7uPt94CihY1F4+kwV9/2acTMmUCJk9O1XLClYoWBcaNs69feknFkRznySeBZs04PnqUU4OSJkoOJChFRtqnE+6/nxvdxUHi44EXXrBLWfbtC1SsmO5P+/zzdq2DvXv5PBEHsZoz5c3L688/B7791mxMDqXTChJ0VqxgSWSXC8iaFdiwIU3L1AHJMacVBg5kbWsAiIgAVq5kbQMP2LmTpxdiY/les3w5UKuWRz61+Mq0aXbGX7gwsGkTG2uITiuIJCc+3r1OysCBSgwcZ+NGu1VvaCjw8cceSwwAFkeyGm+5XHy+JCR47NOLLzz3HNC0Kcf//qs66Gmg5ECCyqhRnCkAgOrVga5dzcYjqZSYCHToYL9b9+wJ3Hqrx7/Ma6/Zn3b9ersiszhESAjw3nt274X331dr51TSsoIEja1bWTQvLo43nCtXMkEQm98vKyStpV+2LBtgZMnilS8VHc3uwImJrMK8bh1QrpxXvpR4y7hxdtnLqlX5TfXgLJMDaVlBJCnrhjMujtfduikxcJwjR9zbJ77/vtcSAwC47Tb7fSUujs8fa/+jOETnzu5TQEmPo8h1KTmQoDBlCgvpAdxj0L+/0XAkLV5/3S5d2LIlm2B42YABQMmSHP/2G59H4iBhYcCHHwIZ/nur69+f9bLlhpQcSMA7duzqG85s2czFI2nw66+sTARw17lV38DLsmUDPvjAvu7VCzh+3CdfWjzl9tvtcsqxsSyOpM6NN6TkQAJe794stw4ALVqwroE4SFyce8WqESOAggV99uXvvx945hmOjx/n80kcZtAgoEgRjhcsAL7+2mw8DqANiRLQli9nTQMAyJUL2LKFx54leX65ITFpTYM77gCWLrWniX3k33+B8uVZWRPg86p2bZ+GIOk1ezbw2GMcFyoEbN4cjLUPtCFR5OJF4OWX7etBg5QYOM727cDQoRyHhnJNyMeJAcDnzaBB9vXLL/P5JQ7SvDnw0EMcHzrEqppyTUoOJGBNnAisXctxRIR7oiAO4HJxfTjpEZNq1YyF06kTj8ICwJo1PEYvDhISwsZM1oajd98F/v7bbEx+TMmBBKR//3Wviz9xIjcui4PMmQP8/DPHxYvbSwuGhIW5JwSRkXyeiYMULw68+SbHiYncqKjNiclSciAB6fXX7fXhdu24VC0Ocv48yxRa3nrLrnZn0B13AG3bcnz6NNCjh9l4JA26d2cBLYD7Vz77zGw8fkobEiXg/PorcO+9HOfPz8qI+fObjckp/GZDYt++9iJ/w4bADz9wWtgPHDvGzYnWCZhffwUaNDAbk6TSDz8ATZpwXKgQXyRy5TIbk29oQ6IEp4QE+0gzAAwfrsTAcXbuBEaO5DhjRmD8eL9JDADgppv4vLJ06qTGTI7TuDHQrBnHhw6pKloylBxIQJk4kSeUALbZfeEFs/FIGrz2mr0J8bXXeJvuZ9q2tds4b97M5504zLhxdvnt8ePtjmwCQMsKEkCOHuVS4qlTvF65EqhRw2xMTmN8WeG774AHH+S4SBEWpsiZ01w817FypZ0g5M7NU5cFCpiNSVJp0CD7SOPddwOLFvnVLJUXaFlBgk9UlJ0YPP+8EgPHuXABePVV+3r0aL9NDAB2bGzThuNTp+xN8OIgPXoApUtz/NtvwBdfmI3Hj2jmQALC2rXssuhyATly8C6uUCHTUTmP0ZmDoUPtd9h69YDFi/3+Lu7QIbZxPnOGoa5axW6O4iBJZ6tuuYWbE/3gZIyXaOZAgofLxRtOK8+NilJi4Dj79wNDhnAcGgpMmOD3iQHA55lVfsHlArp00bF5x3ngATs5OHAAGDXKbDx+QsmBON7MmXY75jJl3GemxSF692ZtA4ClLA1WQkytzp3tPZN//AHMmGE2HkmD0aPtKmkjRwL79pmNxw8oORBHO3+eBY8s48YBmTObi0fS4M8/genTOc6Xz3HHyjJlYo0mS8+ewNmz5uKRNChfnlkewLbOb7xhNh4/oORAHG3UKDvJb9SIM4TiIC4X0LWrfT1gABMEh2nc2O7pc/Cg3StKHCQqyi6KMmMGW28GMW1IFMfauxeoUIGJflgYsH49ryXtfL4h8fPPgRYtOK5YkY1wMmb03df3oB07gMqVgfh4ziZs2QKULGk6KkmV996zO7TVrMkEwUAXUC/ShkQJfD16MDEAOCOoxMBhYmOBXr3s67FjHZsYANzvYrWDiI/XzLQjtW8PVKnC8cqVQd13QTMH4khLlrBmCcDCM9u2AXnymI0pEPh05mDwYE7lApyX//5733xdLzp9mknC0aO8XrYMqFPHbEySSgsXAvffz3GRIjzamCOH2Zg8RzMHErguXXI/kTBkiBIDxzl4EBg2jOPQUGDMGLPxeEiuXHa/KIAzCYmJ5uKRNLjvPuDhhzk+eNDu8xFklByI40yfzqJHABARof4JjtSnj3108aWXgEqVzMbjQW3bcu8BwJnpL780G4+kwejR9hLXqFHAP/+YjccAJQfiKOfPu5epHTuWN57iINHRwCefcJwnj+OOLt5IWJj7RMgbb9h7Y8QhypZlRSuAZb2DcAOJkgNxlLFjWcQM4NGxBg3MxiOpdOXRxf79A7KndqNG3EYB8FRN0joI4hCRkezPDbDnwrJlZuPxMW1IFMc4dIibvc6d42zBhg06oeBpXt+QOHMm8OSTHJcrx2+ig08oXM/GjSz0mJjI/lHbtwMFC5qOSlLlgw+AF1/kuEYNYMUKpx9t1IZECTz9+jExAICOHZUYOM6FCywfaBkzJmATA4D7Djp04PjMGbsHgzhIu3ZA1aocr1oFfPWV2Xh8SDMH4ggbNgD/+599F7ZjB3DzzaajCjxenTkYPZrFKQAeFfvxR0c0V0qPI0c423XmDG84//7bPkYvDvHDD0CTJhyHh7O6lXNrtGvmQAJLz572kbA+fZQYOM6JE3bXxZAQzhoEeGIA8HlqbaBNTHTvAyIO0agRjzcCwJ49wLvvGg3HV5QciN/7+We7Pk7x4uq66EhDhwInT3L8/PP2VG0QePVVoEQJjn/8kTei4iAhITzOaCWzgwcDMTFmY/IBJQfi1y5dArp3t6+HDgWyZjUXj6TBnj3AhAkcZ8kCDBxoNBxfy5IFGD7cvu7eHbh40Vw8kgYREUCrVhzHxNizYAFMyYH4tU8+YUMlALj9duCZZ8zGI2kQGclmAwBLBhYtajYeA556Cqhdm+NNm4CpU42GI2kxeDAzPYDJ7u7dZuPxMiUH4rfOnuX7imXMGKefIgpCq1fbzWvy53dvtBRErG0Wln797AKR4hBFi7p31kpajS0A6aVW/NaYMcC//3LcrBlQr57ZeCSVXC77dAIA9O0L5M5tLh7D6tTh8xhgyX4VRnKgXr3swkiffw789ZfZeLxIRxnFLx0+DJQuzboGYWEsKFOunOmoAp9HjzImPQJWqhSweTOQKZNnPrdDbdnC+geJiWzStHOn/V4jDvHOO+wRD7A17KJFTjp5o6OM4mxDhrgXPFJi4DCXLrkXPBo2LOgTA4CFu9q25fj0aS5ji8N07MjeCwDw22/A/Plm4/ESzRyI39mzh8lAQgKQLRuwa5fKzvqKx2YOpk4F2rThuEYN4M8/nXR35VUHD7IwUmwsC0Ru3QqULGk6KkmV2bOBxx7juEIF7poOCzMbU8po5kCcq39/JgYA9/8oMXCY2Fj3naRJz4gLihQBunXjOCHB/b9KHKJ5c+DOOznesgWYPNlsPF6gmQPxKxs3sj6OywXkzctZgzx5TEcVPDwyczB8ONC7N8cPPQTMnZv+wALM6dPchnH8OK+jo4Hq1c3GJKm0fDl3mQK8g9mxA8iRw2xMN6aZA3GmyEgmBgBbqCsxcJhjx7i/AOC506TVf+SyXLmAqCj7OkhPeDrbHXfYSwuHDwfc8RPNHIjfWLGCP28Ap163b+eeA/GddM8cdO0KvP02x+3aAR995JnAAlBcHFCxol1L56ef2I9KHGTrVh4/uXSJHeF27fL34yeaORBncbnYUMnSt68SA8fZuROYOJHjrFmBAQPMxuPnMmd2r8Lbq5fdXEwconx54IUXOD5zxp41CwBKDsQvLFzI48IAd3JbP2/iIG++ae8k7d6d0z9yXU89Ze81WLOGdXXEYfr1s8sqv/MOsHev2Xg8RMmBGOdy2fvXAGDQIB7xEgeJjga+/JLjAgXcKyPKNWXIAIwYYV9HRnK5QRzkllvsVrHx8UwWAoCSAzFu1iy+twBsfvbkk2bjkTRIuiYUFcUdd5Ii990HNGzI8Z499sqMOEivXvbu6WnTeOzK4ZQciFEXL7qf8x4yRM2VHGfxYu6mA4DwcFaQk1QZMcIuBTF4MHDqlNl4JJXy5rWnPxMT3ZNlh9LLsBj1ySfc8AsAdevapfjFIa7cSdq/v8okp0FEBPDssxyfOOG+1CAO0bkzlxgA1vZYtsxsPOmko4xizIULLFFunZxbutQuOiZmpPoo47x5wMMPc1ypErBuHRAa6p3gAtyePdz8Hh/Pwx47dmhPp+NMmgS0b8/xXXcBS5b4W3VQHWUU/zdxop0YPPigEgPHSUx072k/eLASg3QIDwc6deI4NpYbc8Vhnn+evRYA3u18953RcNJDMwdiRNLysSEhwNq1QLXheGvxAAAgAElEQVRqpqOSVM0czJhhz4XXrMkqVv51l+Q4R4/y5+LsWfbx2bKFrcvFQZI2ZapShS9u/pM0a+ZA/NvYsXZd+WeeUWLgOAkJrFRlGTpUiYEHFCjAEhEAN+sm/S8Wh2jeHKhVi+MNG4DPPjMbTxpp5kB8TndH/ivFMwfvvw+89BLH99wD/PKLdwMLIppVCwCLFwMNGnBcogR3XWfObDSk/2jmQPzX0KFMDADu3VFi4DDnzwMDB9rXQ4eaiyUA5cplHwBxudy3dYhD1K8PNGrE8T//AO+9ZzSctNDMgfjU3r08oWDtyN65Eyhc2HRUYknRzMHIkXYbwWbNgDlzvB9YkNFJngCwZo1dG/umm/hiZ744mGYOxD/178/EAGDFUSUGDnPqlN2GOSSEJxTE47Jkca/C26eP3cpcHOLWW7mhCmAr8zFjzMaTSpo5EJ/ZvJmbdxMTWWl01y4WFhP/ccOZg6goOyF47jmWihWvuHiR3YC3beP1998DjRubjUlSaedO9uVOSABy5OCLXoECJiPSzIH4n8hIuyVtr15KDBzn8GFg3DiOM2ZUS2YvCwtzn5jp00ctnR2ndGm7KNLZs45q6azkQHxi1Soe/wWAQoWALl3MxiNpMHQocO4cxx06ACVLmo0nCDz2mHtL56+/NhuPpEFkJDdYAaz8tm+f2XhSSMmB+MSVTfuyZTMXi6TBP//w+CLAF7qk3bLEazJkcD8MEhnJGWpxkMKF7buhuDj3kz5+TMmBeN0vvwALF3JcqhTQrp3ZeCQNBgxw30laqJDZeIJIw4bA3XdzvH07MHWq0XAkLXr2tE8qTJlibyTxY0oOxKuubNo3cKCa9jnO5s1snwlwJ2nPnmbjCTIhIe5L1QMGsPeCOEi+fECPHhxfuuR+FMVPKTkQr/rmG2DlSo6rVrVP9oiDREXZO+F69tROUgPuuAN46CGODxzg0rU4TNeu9kmFL75g6Us/puRAvObSJffqbkOHcg1VHOSvv4BZszguWFA7SQ0aMsRuXzFsGMssi4PkyOH+gujn+3b0Ui1e8+mnnJEGgDp1gAceMBuPpEHSF7OoKCB7dnOxBLmqVYEWLTg+ftxxNXUEADp2BIoV4/i774Bly8zGcx0qgiReERcHlCvHcskAsGQJULeu2ZjkxtyKICVtHhMezuYx2jBi1M6dQIUKLJDkHzV1JNU+/hho25bjevX4c+a7jqYqgiRmvf++nRg0aaLEwHFcLqB3b/t6wAAlBn7gypo66nnlQK1a8c4J4F3TTz+ZjecaNHMgHnfmDF/Ejh7l9erVLDMu/u/yzMHcucAjj/DBypWBv/8GQkPNBicAgH//5c9XbCzzte3bgeLFTUclqfLVV8BTT3FcvTr39vhm9kAzB2LOuHF2YvD000oMHCcx0X2vweDBSgz8SNKaOvHxqmLtSI8/DkREcLx6tV0+1o9o5kA86tgxFjo6c4bvJ5s3s/WsOEPRokWxf8QIoGVLPlCzJrBihS/XRCUFYmJYvfrUKZ4A2riRexHEQb7/HmjalOMKFYD169lQw7s0cyBmDB/OxADgnhslBg7Ut689HjpUiYEfypvXrkWVmOj+LROHaNwYuOsujrdsAaZPNxvPFTRzIB6zbx+Tgbg49qPfsQO45RbTUUlqFM2bF/tPnuTFvffada/F75w7x1m6I0d4HR1tN2kSh/j9d55YAIASJXgiKHNmb35FzRyI7w0cyMQAADp3VmLgOOfPu1fW0VZ4v5Y9u3sdnaRlysUh6tblcS6Azc0+/NBsPEkoORCP2LqVx3cB9hfp1ctsPMFs+/btqFOnDsqVK4eaNWti06ZNKfuL77xjl0lu1oz7DcSvdejAG04A+PFH4LffzMYjaTB4sPvYaotumH8kBy4XN2OIY11Zfj9/frPxBLOOHTuiQ4cO2LZtG3r27Im2VsGV6zl5khtGAO4xSPqCJX4rc2b30wp9+vDlVBykenXgiSc4PnIEGD/ebDz/Mb/n4NdfeZu5bh3bWFppsDhGdDRw++0c33wzq7jlyGE2pmB15MgRlCtXDseOHUNYWBhcLhcKFy6MFStWIDw8/Np/MTISGDIERQHsb9XK7sIofu/SJZZWtkqVz5sHPPig2ZgklbZuBSpV4h1WnjwsfemdBmcp3nOQ6uSgQoUKrrNnz6Y6oms6fdre3p4tmzq+OdCxY/Zegzx5VH7fpISEBJw4cQIFCxa8/NiRI0eQJ08eZEqmwuHZs2dx/swZFEhMRAiAfwEULlRIdQ0cJjYWOHGC44wZmaSLw8TEcN8PAOTMyfVZDztw4MBWl8uVokOv5mcOYmK45fbkSR7Y3bABqFjRo19CvCdp+f3Q0L04f764quwaFB0djVatWmHjxo2XH6tRowbGjBmDetau6Cu9+urlqcx8ISE4Ya0PiWO4XECtWsCqVbyeMUPt0R1n716gbFlEu1y47eefgbvv9sZXcdBphSsP7EZFmY1HUuzK8vu5co1RYmBYsWLFsH//fly8eBEA4HK5sG/fPhS/Vn3df/5hIwwAyJYNZ1XTwJFCQtwPl0RFAQkJ5uKRNCheHFi9Go8UKOCtxCBVzCcHANClC45k+C+UWbNYZ1r83ty5LJ4HsPx+1qxzzAYkuPnmm3Hrrbdi+n8FVWbNmoXw8PBr7zfo3581eAHg1VdxySdRijfcey+QKRNbAO/caZ8eEgepXNlvio75R3KQPTs2WE1eAPe67uKXLl1y/zYNGQJ0797VXEBy2QcffIAPPvgA5cqVw/DhwzF58uTk/+CmTcC0aRznyQP06IGcOXP6LlDxqJAQ4MUX916+HjiQexHEWbp162Y6BAD+sOfAEh8PlC8P7NnD60WLgPr1vfKlJP2mTQNat+a4dm3gjz/8JuGVlHrsMbvhy7BhwBtv2F0ZxbGaNQO+/ZbjUaOA1183G4/4Fe+dVoA3yyfrHccRlMcFgFWr7CJHBQtyHjp7diUHAWDDBqBaNe4JypePp+Jy5zYdlfgJB21ITOrZZ3nWE+Bi9vz5ZuORZH34oZ0YNGyoxMCRkq4JRUXp/GkAqVKFL6UAjzeOHWs2HnEm/5o5AIA5c4BHH+W4alVg7VoecRS/cPYsULq03ezlr7+A224zG5Ok0qJFwD33cFyyJDvC/XfMRDMHgWHXLs7uXbzIgmS7dgEFCpiOSvyAQ2cOAC6Y1ajB8fr1wOefm41H3Lz9tp0YPPGEEgPHufL86YAB0PnTwFOqFNC+Pcdnz6qHlqSeXyUHMTExKFqsGB7dsuXyY7tatsRL1rNcjDp+HBg5kuPQUGDQoOv/+ZkzZ+Kll17ChQsX0KxZM5QrVw4RERFo3Lgx9ljrEuJb8+YBf/7JceXKQIsWbo2ajhw5kvJGTeLXoqKATJlY72LiRBcaNmynn0E/VL9+fb/8XvhVcpA3b160aNECt7/xxuVpz1IA3tPtqV8YMcLu6NumDactr+ebb75Bs2bNAAAdOnTA1q1bsXbtWjz44IPo0KGDl6OVq1x5/nTwYCA01K1RU86cOVPWqEl8LrVvIoULA2XKLAAAxMeHICSkn34GHSImJgZFixbFTTfdhIiICFSsWBGZM2dGx44dfRaDT5ODunXr4qabbkr2Y9++fQCAtWvX4tZbb3WfBxs40K45LUYcOABMmMBx5sxAv37XfwInJCRg2bJlaNCgAbJkyYKmTZsi5L+TJ7Vr18auXbsM/muC1IwZ3MoO8KTCI4/gyJEjWL16NVq2bAkAyJo1K3bv3u2XdzJytRv9DJ4+HYXcublNbOHCYti2TT+DTmDdKHfr1g1r167FW2+9hTvvvBMffPCBz2LwaXLw+++/49ixY8l+FCtWDACTg4iICKBWLawsXJh/8d9/gXff9WWocoWBA4ELFzju1AkoWvT6T+BFixahTp06yTb7GT9+PB566CEf/wuCXHw80LevfT1sGBASgn379qFIkSIICwu7/FvFixfH3r17k/kk4m9u9DNYt25l9OjBhCAx0X4K6GfQrDZt2iAiIgIRERH466+/0LRp08vXV90ogz1TIiIifBqjXy0rHDhwABkyZEDh/5KC/mFhcFl1DoYPB06dMhhd8LFmevLmrYkPP+TaZUjIGUyZUu6GT+BvvvkGzZs3v+pzDh06FNu3b8eQIUN89K8QAO7nT++/3z6tAFye0bGk4QSTeEl63kSsn8FXX7W7NH71FdCp02T9DBo2ZcoUrF27FmvXrsXtt9+OBQsWXL6+6kYZZpIDuFyu1H54zbx581yNGjW6fF29enXXH2XKuFzcY+1yRUZ688vLNTz9tP0tGDDA/fcKFCjgOnjwoMvlcrkeffRR1yeffOJKTEx0lSpVynXmzBm3Pztq1CjXbbfd5oqJifFV6OJyuVxnzrhcN99sfxNXrbr8W4cPH3blypXLlZCQ4HK5XK5bbrnFVbBgQdfu3bsNBSvXcvfddyf7fUnJz+Dbb9vf/ly5lupn0I8k933dv3+/q2DBgpevy5Qp41q9erUnvlyK3+v9aubgwQcfxA8//HD5Ojo6Gnf89BMblAPAuHH2OTrxiTVrgC++4LhAAeC11+zfu3KmZ926dahatSpWrlyJihUrIkeOHJf/7NixY/H555/j559/Rp48eXz5T5Arz5/efvvl37qyUVNsbOz1GzWJX0npz2DHjkDevJx5PX36Tqxbp59Bf7ZmzRq3mYJcuXJh4sSJPo3Br5KDZJUsaR/YPXdOB3Z9LOnm9jffBJL25bnWE3jOnDmXTykAwP79+9G9e3ecPHkSDRo0QEREBGrVquWL8CUF50+TNmo6c+bMtRs1id9J6c/g0aP7ERPz6uXrJk3WoGZN/Qz6g8WLF1+VjCd3o/zRRx/5NC7/q5CYnH//ZVm+2FgWbNm2DShRwudhBJslS+y24sWL8789c+Yb/73KlStj0aJFuNla6BRzevQARo/muF074AYvMKqQGBiS+xm8eJFFZ60yMvPnAw88YChAMcXBFRKTU7gw8Op/WW98PKu6iVclV0gvJYkBAGzcuFGJgT/Yvx945x2OrfOnEhSS+xkMC2NpC8ubb/IEg0hynJEcAEDPnnZrsU8+sdNf8Yp589gUEwAqVgSee85sPJIGSc+fvvIKz59KUHv0Ubvk+d9/8/SCSHKckxzkzcsEAWC6GxVlNp4AdukS0KePfT10KJerxUG2bQM+/pjjXLncp4EkaIWEuG/biooCEhLMxSP+yznJAQC3A7tffw1ER5uNJ0BNnw5s3Mhx7drAI4+YjUfSICqKWR7AfQf585uNR/zG/ffbbdZ37ACmTDEajvgpZ2xITGrCBKBLF44bNQKS7OiU9LtwgT0TrAJ5ixfbmxLFIaKj7eOKN98M7NzJvr0poA2JwWH5cqBOHY5vuQXYvh3ImtVsTOITAbYhMakOHeyTCj/+CPz2m9l4Asx779mJQZMmSgwcKemaUGRkihMDCR533AFY1ZMPHAB8fIReHMB5MwcANyQ+/zzHd9wBLFvGxTRJl1OneGL0+HFer10L/O9/ZmOSVFq0yC6NHB7OjbspPWYCzRwEk/Xr+fPtcnHVadcubk+RgBbAMwcA0LIlt9ADnB+bP99sPAFizBg7MWjRQomB46Tn/KkEnapV+XMO8Od+7Fiz8Yh/cebMAQDMng089hjHVarwNldb6tPs8GHOGpw7x2rVW7YApUqZjkpS5ZtvAKvZVeXKPKuWyp8JzRwEl507gQoVWCApRw7OHhQoYDoq8aIAnzkA+CJYsybHGzYAn35qNh6HGzSIiQHAOuxKDBzm0iX3Wtc6fyopULo0C2cCwNmz7OQtAjh55gDgZkTrTE7RojzbrS23qZb07iF7dl4XLGg6KkkVD+3D0cxB8Dl4EChThtXpM2fmy2jx4qajEi8JgpkDgFvpreLg+/fzmKOkWt++TAwAoFs3JQaOExfHb6Jl2DBt0JUUK1IE6NyZ47g41ZcTcvbMAeC+5TZPHt725stnOirHWLsWuPVWjm+6if992rHsMOPH271HGjcGvv8+zZ9KMwfBKSaGSwwxMcwr16zRhuQAFSQzBwC33LZuzfHJk1o0S6Wkm9vffFOJgeOcOuXehlktzSUN8ua1t6y4XMAbb5iNR8xz/swBwKo95cpxTkyLZim2eDHQoAHHqWnJLH7kzTfthOCZZ4AZM9L16TRzELyurI66cCFw771mYxKPC6KZA4DvbFZJ5SvXXyVZV94dDByoxMBxDhwAxo3jOGNGYMgQs/GIo2XJ4t7SuWdPtXQOZoGRHAB8p8uTh+Np07gXQa7pm2+AP//kuEoV1pUSh+nfn1vMAaBTJ6BkSaPhiPM9+6y912D1auDLL83GI+YETnKQL59dU16LZteVkOD+36Mj8Q60aZN7S+akNQ5E0ihDBmDkSPv6zTc5GSvBJ3CSA4DncYoV43jBAi6qy1UmTeL+AgC46y7gwQfNxiNp8MYb9pxv7948aiLiAQ0bAvfdx/Hu3cD775uNR8wIjA2JSU2dCrRpw3HNmsCKFTrzncSZMyx4cuQIr1esAGrVMhuTpNKSJXa7TA/329WGRAG4pHDbbRznz88jzrlzm41JPCLINiQm9dxzXEQHgJUrgVmzzMbjZ0aOtBODJ59UYuA4Lhd3ilkGDVJVUPG46tW5/wBgU6akSw0SHAJv5gAAvvvOnisvWxbYuJG7uYPcgQP874iN5X/H5s0sfCIO8vXXwBNPcOyFhmOaORDLnj082hgfz/xz+3ZOVImjBfHMAQA0bWpPu27fzkV2Qb9+9ub2l19WYuA4CQnuVatGjNBOUvGa8HAeggH4utGvn9FwxMcCc+YA4Dm92rU5LlgQ2LGDPUmD1IYNPKKUmMi1w507uZYoDvLuu8Arr3Bcvz7w668e30+jmQNJ6vhx3kScOsWTDOvXA5UqmY5K0iHIZw4ALqY//jjHhw8Do0aZjcewpAVN+vRRYuA4p08DAwbY1yNHaqOteF3+/PZkVWKiTogHk8CdOQC4pFCpElsOBvGi2S+/2EeTihcHtm5lNTRxkL597R4KTz0FfPGFV76MZg7kSrGxrE5vPS1++w2oV89sTJJmmjkAwN13SRfNgrBQTGIi0KOHfT14sBIDx9m/Hxg9mmOVSRYfy5qV5dUtPXqorHIwCOzkAOAdV9KyyqtXm43Hx2bMYPtVgK2ZreNJ4iB9+tg7SV95RTtJxedatXI/Ia6yyoEv8JODfPnsRkwuF9C9O38NAhcuuE+WjBrFTUXiIH/9BXz6Kcf58gFRUWbjkaAUGmpPXgFAr152viqBKTjeKjp1YllAgCWV5841Go6vjB9vt19t0kTtVx3H5QK6dbOv+/UD8uY1F48EtUaN+DoCAPv2AWPHmo1HvCuwNyQmNXs28NhjHJcty7N9mTKZjcmLjhzhP/P0aW5q//tvoGpV01FJqsyaZZ+4KVeOz1kvF/PShkS5nk2bgGrVgEuXgOzZeUK8UCHTUUkqaEPiVZo3B+rW5Xj79oDvJhIVxcQAANq1U2LgOHFx7mWSR49WlU8xrlIloGNHjs+dAyIjzcYj3hM8MwcA129r1OA4Xz6mvQE4Tfv336yNnpgI5MzJf+bNN5uOSlJlzBjg9dc5vuceYOFCn9Q10MyB3MjRo5yVPHWKT8nVq4GICNNRSQpp5iBZt98OtGzJ8YkTPNcXYFwu4LXX7KNGUVFKDBzn2DG7pkFICBMFFTwSP1GggD1jEGR7vINKcCUHADB0qH3Qf8IE1hEOIHPnAosWcVyqFNCli9l4JA369+dtGQC88IJuy8TvdO5sn6j99Vdg3jyz8YjnBV9yUKwYU12AjWx69TIbjwfFxdn/NIDL1Jkzm4tH0mDzZns/TPbs9gyCiB/JnNm9jfPrr7N7owSO4EsOACYEBQtyPGsW8PvvZuPxkKQTIfXrA82aGQ1H0qJHD24FB1jIvnBhs/GIXEPz5nYZ5e3bgffeMxuPeFZwbUhMatIkoH17jm+9FVi1ytHtb688urhmDbswioP8/DPQsCHHxYqxCUbWrD4NQRsSJTWio7mVC+De7h07uNdb/JY2JN5Qmzb2Wu6aNUwWHKxvX/eji0oMHObiRfeCR8OG+TwxEEmt225jaWUAiIlxbxwqzha8MwcAsHSpXfsgf35g2zZHpr3r1nHywzq6uH27vWoiDjFhgr17tGZNYPlyI7WuNXMgqXXgAGctY2OBsDAepa5UyXRUcg2aOUiRu+4CWrTg+Phxlqd1mCuPLkZGKjFwnKNH7f4fAOteqwmGOMQtt3B7DMAJsFdf1dHGQBDcMwcA097y5VnuK0MGYO1aR5UT/PZbe+NhqVIsb6oTCg7TsSPw4YccP/88MGWKsVA0cyBpERsLVKwI/PMPr2fP5oZF8TuaOUixW26xWxcmJnJq1yFp74UL7svUo0YpMXCc1auBjz7iOGdO7jUQcZisWd0bMXXrpq6NTqfkAOC8vFXRY/Fi4OuvjYaTUqNGAbt2cdyggTJ1x3G5WE3GSkb79VMXG3Gs5s2B++7jeM8evj6Jc2lZwTJvHvDwwxwXKwZs2QJky2Y2puvYs4fTeBcu8ATm338DlSubjkpS5bPP7HLe5ctzZ6nhTqFaVpD02LSJJ6UuXmQh2i1bgBIlTEclSWhZIdUefBBo3JjjffuAESPMxnMD3boxMQC4EqLEwGHOnGHBI8tbbxlPDETSq1IlToYBfH2yeoeJ82jmIKmtW4EqVZj2Zs7MUrYlS5qO6io//mjnMQULMuzcuc3GJKnUuzcwfDjHDz/MnaV+QDMHkl6nTgHlyrEwG8CGovfeazYmuUwzB2lSvjzQtSvHcXF+mfbGxbk3Uxo1SomB42zbZu/eypTJfSeXiMPlzm3nvQBfrxISzMUjaaPk4EpRUXahgNmzmfb6kbfe4nsLANx5p71kLQ7hcgGdOtldal5/3d4MKxIgWrcGatTgeNMm1vgSZ9GyQnI++YTnzQHOj61b5xdnBPfvBypUsEsyREerm6/jfPkl8PTTHJcowVdOP9r4qmUF8ZSVK4HatZkP58jBVdqiRU1HFfS0rJAuzz0H1KnD8bZt7r1JDXr9dSYGAPDSS0oMHOf0aR6btYwf71eJgYgn1azJ+l4AcPasvWIrzqCZg2tZtw6oXp3tczNnBjZsAMqUMRbOTz8BjRpxfNNN3ITowDYQwa1rV+Dttzn2o02ISWnmQDwpJoazndbmxAULgCZNzMYU5DRzkG7VqrlvTnzlFWOVE2NjgZdftq+HD1di4Dhr19oLr1mz2kmCSADLmxcYPdq+fuUVVU50CiUH19O/v71I9uOPxionDhkC7NzJcd267DYtDpKYyHUgqztWVBQQHm40JBFfadkSqF+f4127gKFDjYYjKaRlhRuZMwd49FGOixThrppcuXz25TdvZsWxhAQgY0begKodqsNMmgS0b89xhQosZ+mnBY+0rCDekPR1LFMmrtqWL286qqCkZQWPadYMeOABjg8edG+t62WJidzQY50R7tFDiYHjHDsG9OplX7/7rt8mBiLeUrGiXTYmPp6neR3S3y5oKTm4kZAQrhVnzcrrCROANWt88qWnTgV+/53jUqWAyEiffFnxpO7dgRMnOG7RArjnHrPxiBgSGWn3WfjlF+Dzz83GI9en5CAlSpbkOjHA2/kOHXiKwYuOHnUvvf/ee3Z+Ig7x00/AtGkc58kDjBljNh4Rg7Jlcy+G1LUrcPy4uXjk+pQcpFT37pwbA4C//vL6bvMePewbzqefBho29OqXE087d84+5A1wy7baMUuQe+gh4LHHOD561L3sh/gXbUhMjWXLeFzA5eJt/IYNnO/3sF9/tRuV5M7Ntqd6X3GYbt2AceM4btCA86ghKd4LZIw2JIq3HTrE+6yTJ3n9/fd2IznxOm1I9Io77+ROGoCHddu39/iumnPngHbt7Othw5QYOM7KlfbMUpYswAcfOCIxEPGFQoXcax+8+CIrKIp/UXKQWkOHAsWLc/zrr8CUKR799G++CezezXHduu4z0+IACQnM7qyaBv37A2XLGg1JxN+88IK9N/eff7TZ2h9pWSEtfvjBrgGaJw+b5xQunO5P+8cfwF13cTIiSxaeBdb7isMMHcoMD2Dzi5UrWaDCIbSsIL6ycydQtSonYUNC+PpXu7bpqAKelhW8qnFjNmcCuHD2yivp/pQXLjCbtnK1QYOUGDjO1q3AwIEcZ8jA4kcOSgxEfKl0afvHxeXihJvVyVzMU3KQVuPGAQUKcDx7NjBrVro+3YABfG8B2M1Mu3gd5tIloG1b9uEAuCHxttvMxiTi57p2tX9MNm7kHivxD1pWSI8vvgCeeYbjggX57M6fP9WfJjoaqFWL7y8ZM7LGUuXKHo5VvGvMGLsEXKlSwPr1jmzHrGUF8bW1a4Hbb7df/1auVDt6L9Kygk889RQP7gLA4cNpWl6Ij+dyglVTKSpKiYHjbN5s7zMICeEmVQcmBiImREQAb7zBcUIC0KqVPQEn5ig5SI+QEOD999mXFOBMwpdfpupTDB3KjYcAG5NYPyTiEBcvAq1b269mr70G1KtnNiYRh+nbF6hWjeP163nIR8zSsoInJF1eyJePxZFScHph5UqgTh3OGoSG8rp6dS/HKp41ZIh9DqtCBWD1akfXudaygpiybh2XFxISuJ936VLgjjtMRxVwtKzgU08/DTz5JMcnTnDb7Q2SrnPn2OfcWk6IjFRi4Dh//82dpABfzT75xNGJgYhJ1arZP06JiZyQO3fObEzBTMmBp0ycaJcyXLCAx9iuo2dPYPt2jmvUsJesxSHi47k4avXTfuMNHjMRkTTr0cOudbB9O9C7t9l4gpmWFTzpu++ABx/kOEcOzpOVLHnVH/v+e6BpU46zZuXphPLlfRinpF9kJJcUAN7yrFwJZM5sNiYP0LKCmLZtGzcpxsby+pdf1Oncg7SsYMQDD9iNEc6e5bzYFa2djx/n6QTLmDFKDBxn6VL7QHZYGJcTAu/y+sYAAA+WSURBVCAxEPEH5coBw4fb123a2E2axHeUHHja2LFAeDjHv//u1mHE5WKvhEOHeN24MZuOiIPExAAtWrj3TtChbBGPeuUVNjMFgL17+brp4R53cgNKDjwtZ07eSVpd+CIjgRUrAADTp9uFFPPnBz7+WM36HMXlYifOfft4fffdOnsq4gUZMgBTp7J1DQB89RVfL8V3lBx4Q716QJ8+HF+8CDzzDHatPnm52zMAfPihR3o1iS9NmmRnd3nzAp9+yjOoIuJxxYu77+vu3Jn1xsQ3lBx4S//+wJ13crxnD3bc2wFnznBerHVr4NFHzYUmabB5M/Dqq/b15MlAsWLm4hEJAo89Zretj43lqfELF8zGFCyUHHhLWBjw2WeX58UanpyJdpiEChWAd94xHJukzoULfFWytk+/+CLQvLnZmESCxNixdkn5det43FG8T8mBN5UogT/aTb58OR5d8O3QjciRw2BMknq9etk1ritV4hETEfGJbNlYhDZLFl6/8w4wd67ZmIKBkgMv2rkTaPLho5iIlwAAWXEB5aKeAs6fNxyZpNi33wLjx3OcOTNfpdRUScSnqlThDIKlTRtA5Ti8S8mBl8TFsWnj6dNAd4zB3txV+RsbN7qvXYv/2rGDG0QsY8YAVauai0ckiCVdzTtxghXr4+PNxhTIlBx4Sc+eQHQ0x8XKZkX+hV/adfcnTdK5HH8XGws8/jhw6hSvn3gCePllszGJBLGQEL50Fi/O6+XLgddfNxtTIFNy4AVffeU+E/3VV0D22yuyvbPl5ZfZwU/8U6dObKwEsITl5MkqSiFiWL58wNdfA5ky8XrCBODzz83GFKiUHHjY+vVcD7OMG5ekgF6rVvbdZ1wcz+kcP+7zGOUGJk8GpkzhOFs21jbImdNsTCICgI3qJkywr9u142qteJaSAw86cQJo1szeb9i6dTLlkceNs9uO7dkDPPvsVf0XxKDly92XDz780D5HJSJ+oX17ezvQ+fOsG6P+C56l5MBDLl3i+/yuXby+7TbgvfeSmYnOlAmYORMoUIDXP/6ofs3+4sABvspYu5w6deI3VUT8SkgIMHEi8L//8XrbNpYi0X2W5yg58JDISOCHHzguUACYPdvef3iVokWBL7+0S++OGMFSvGJObCy3QltdserX5yyPiPilbNmAOXPYpwbgfVavXmZjCiRKDjxg6lS7xWhoKDcgWjtqr6lBA+Dtt+3rdu0uN2gSH3O5gA4dgFWreB0eztmdjBmNhiUi11eyJDcohoXxeswY9r2T9FNykE6//cb3Fctbb/GmM0VeftnelBAfzw0LVsc/8Z3hw9kyE+DtyLffAjfdZDYmEUmR+vXdNyh26AD88YexcAKGkoN02LGDS9QJCbzu1Il9yFMsJIRnHq1s4vBh4MEHWTlJfOPzz+0OmgAwbRpQrZq5eEQk1V58EXiJhWgRHw888ghfnyXtlByk0YkTwAMP8FcAaNSIswapljEj58VKleL1unU84qjSX973++/A88/b18OG8f9eRBzn7beBe+/l+NgxoEkT/ippo+QgDc6f5w3+tm28rlSJ+wutda9Uy58fWLCAFT4AYOFCntVxuTwSryRj61beXlhJWPv22s0k4mAZM7IkSZUqvN6xA3j4YbuZqqSOkoNUuniRPROWL+d1wYLA/PlA7tzp/MTlywPz5tmtx6ZNA/r2TecnlWQdPMjbipgYXjdqBLz7riogijhc7ty8zypShNfLlwMtW+qIY1ooOUgFa1P7/Pm8zpmTxxdLlvTQF6hTB/jsM/tNavBg9502kn4nTgANGwK7d/O6WjUeL9HJBJGAUKwY8N13QI4cvJ49G+jYUROxqaXkIIVcLqB3b7uqbqZM3NR+uTSypzz6qPvmhS5deFZS0u/sWaBpU7vWang48P33QK5cRsMSEc+KiOASg5XzT54MdO+uBCE1lByk0MCBrFUE8MZ++nSWKvCKLl1YVcnSti03LUraxcWxyNGff/K6UCHu7bDmH0UkoDRsyInYDP+9y40bx8lYSRklBykwbBjQv799PXEiO/h61cCBTBIAIDERaNGCi2mSelaTq4ULeZ0nD8uplS5tNi4R8aonnmB7FEvfvsDYsebicRIlBzcwdqz7Mfhx45JppuQNISH8YlaLx4QE3vlaGx4kZazE4LvveJ0tG8eqZSASFNq2ZeVES/fuwKhR5uJxCiUH1zFqFJ9IlhEjgK5dfRhAhgzARx8BTz7J6/h47kn45hsfBuFgFy7w/ytpYrBgATd+ikjQ6NaNk7GWnj3tkveSPCUHyXC5gKgoPoEsAwe6X/tMaCgXzp5+mtcJCZwrmznTQDAOcu4cZ1qspRgrMbj7brNxiYgRUVHuew569waGDDEXj79TcnCFxETgtdfcn0SDB/OJZUxYGHdAPvccry9eZLLw8ccGg/JjJ04A999vt8nMlo2nEpQYiAS1N9/kHjJLZCRnhxMTzcXkr5QcJJGQwPWppM0S336bTyjjQkN5jrJtW14nJnI8eLDO5yS1fz9Qt65dpSpXLiYJ9eqZjUtE/MIbb7jvORg7FmjVShXrr6Tk4D8nT7JonlVSIEMG3phbBwb8Qmgot94m3fgQFcXujioBBmzeDNx5J7BpE68LFmTbzLp1zcYlIn7l9de5ncs65vjZZ8BDDwFnzpiNy58oOQCwZw/fU375hdeZM7NXgnVQwK9kyMBUN2nq+/77XF8P5m6O338P1K4N7N3L61KlgGXLvFClSkQCQbt2rJ5oVaz/6Sfgrrv4fiBKDvDHH3xPsW428+dnkvD442bjuq6QEKa+06fb3Z7mzQPuuCP4+pS6XDzymbTVdUQEEwPVMRCR63jkEeDnn1n6BGBT3Bo12LA12AVtcuByAePHc4/a4cN8rGxZYMUKziI4wrPP8o7ZemZv2sRn9o8/mo3LV2Jjue+iWzd7R1Hz5sDSpayAKCJyA3fdxS1KZcvy+tgx4J57uIIbzNu5gjI5OHuW76uvvsqN/wBQvz6fIGXKGA0t9e67D1i1in2jAW6eaNqUJR2tf1wg2rIFqFnTbnYBcOvx118D2bObi0tEHKdCBVZWv/9+Xl+8yGZNrVoF7z6EoEsOoqN5c/355/ZjPXpwail/fnNxpUuZMpzyeOQRXicmAgMGsPmDtQYfSKZNA267DdiwgddZswIzZgCDBtk7jEREUiFvXpZCee01+7Hp04Hq1YHVq83FZUrQvJJevMj3jtq1edMJsOXy118DI0faS/eOlTMnd9cMGsRTDQCn1//3P+6uDIT5saNHWd+hdWvg/Hk+Vrky8NdfwDPPmI1NRBwvLIz7vb/4wm7WumMH3zdGjAjsydgrhbhS/6bhuHeZDRu4M9VqyAcwG5wxAyhf3lxcXvPHH2zU9M8/9mMPPwy88w6bnTuNy8UEp3NnLghaXngBmDCBRY7EI4oWLYr9+/ebDkPEuF27eC+yapX9WPXqbP/s4ENQISn9gwE9c3D6NPeqRUTYiUFoKEsDrFgRoIkBwN4Ba9cCTz1lPzZ3LvclvPOOs2oi7NwJNGvGmQErMciXj5nd5MlKDETEK0qV4uRrz572auXq1cDtt7P08tmzZuPztoCcObh0ie8dvXoB//5rP16uHJera9UyF5tPuVzArFm84z50yH68ShXOkTVpwmOR/ujUKVZ/fPttlq60PPYY8O67LHAkHqeZA5Gr/fknD0Zt3Gg/VqgQV3HbtLFXch0gOGcOEhPZj6hqVe4ytRKDLFn4Tfz77yBKDAC+8T/+OI84tm9vP75hA/DAAzyvk3StxR+cOQOMHs1zRaNH24lBoULAV19xk4gSAxHxoVq1OGswYACQMSMfO3SIL6sREcC33wZef4aAmDm4cIHvG2PGsIhFUg8/DLz1FlCypJnY/MrSpewysnKl++P16vHIRtOm5nb7nzjBwhPjxwMxMfbjWbIw5l69uOlSvEozByLXt307X47mzHF/vGJFLkG0aAFkymQmthRI8cyBo5OD3btZH/ujj9z3qQFcdh80iDfHkoTLxbvv3r25np9UxYqcO2vRAihc2PuxJCYCixdz78Ds2czyLCEh3A00bBhQooT3YxEASg5EUur333nfknTDIsCXztat+VGhgpnYriNwk4MdO7iMPnMmaxZcqUYNYOBAoFEj/11O9wvx8dyAMWaMfbbTkiEDcO+9wJNPAg0bAsWLe+7rJiRwBuO775gQ7N7t/vuhoUDLlkzNK1b03NeVFFFyIJJyLhdrI4wYkXzJ5dq1ubLbuDH3g/vBe1JgJAcnTnAKJzqa7ydLlwL79l395zJm5DfglVfYXsAPvgHOkZjIN+rRo4ElS5L/M+XLsxJj9erc0FG5cspOCbhcwJEjXKyLjmY9gkWLkm8QlS8fk4LXXgPCw9P1Twp2Xbp0wdy5c/HPP/9g/fr1qFKlSor/rpIDkbT54w++jM6bl3w9hGLF7JfRiAigWjW7loIPOSc5WL4c+OQTHguxPk6e5BnTpEvPyaleHXjiCeD551VK3yO2bWNJsOnTr76jTyokBChSBChQgB/58vHxS5f4ERMD7N8PHDjA/gfXYs1QtG3L6o5WezRJlyVLlqBUqVK46667MH/+fCUHIj505AhPy02ZcvUeuCvlz89liEKFOM6cmfsV2rblrIMXOCc5mD4deO65lP3ZbNk4M9CwIWcKSpXyZCRymcvFUww//si60itWeK42Qt68nGN74AH+6tia1f4vPDxcyYGIQVu2AD/8wI/Fi4G4uJT9vRkzvFb0NcXJgfGiwTlyXP1YSAiXucuU4UeFCuyUGBFhHyMRLwoJYdpauzbQrx9rDqxaBaxfb38cOMByxteqJ5ozJ3DLLUDRolyKuO02Vg8pW1b9D/zI2LFjMXbs2MvXZwO9souID1WowI+uXTmJum4dsGYNa9StXw8cPMgj91cmDf5w2sH4zMHJk8CePUwSrI9s2fT+4QguFxOHmBgmFKGh/MiZU8cOvaRu3brYvHlzsr+3Zs0aFEtSHlszByL+z+XiNqwTJ7hfOy6O+xPy5PHKl3POzEGePI6uUx3cQkL4DfTSs1iu9ntyW6JFxLFCQoDcufnhT3R/LiIiIm6UHIgEmE6dOl1eHrjvvvtQpkwZ0yGJiMMY33MgIv5Dew5EAlpwNl4SERGR9FNyICIiIm6UHIiIiIgbJQciIiLiRsmBiIiIuFFyICIiIm6UHIiIiIgbJQciIiLiJi1FkEQkQIWEhGxxuVwVTMchImYpORARERE3WlYQERERN0oORERExI2SAxEREXGj5EBERETcKDkQERERN0oORERExI2SAxEREXGj5EBERETcKDkQERERN/8HBMZt+pqfF+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8,5), dpi=80)\n",
    "plt.subplot(111)\n",
    "\n",
    "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n",
    "C = np.cos(X)\n",
    "S = np.sin(X)\n",
    "\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('data',0))\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "\n",
    "plt.xlim(X.min() * 1.1, X.max() * 1.1)\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'])\n",
    "\n",
    "plt.ylim(C.min() * 1.1, C.max() * 1.1)\n",
    "plt.yticks([-1, 0, +1],\n",
    "          [r'$-1$', r'$0$', r'$+1$'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
